ON THE ESSENTIALITY OF BANKS*

We compare allocations sustained by credit with allocations sustained by bank notes (inside money) in a search model with decentralized trade and limited monitoring. We demonstrate that there exists a credit arrangement that is superior to inside money. However, in contrast with inside money, this arrangement is not robust to an expansion of trade that is not accompanied by an adequate increase in the degree of monitoring. Therefore, banks are essential when trade is intense and monitoring is limited. As a historical application, we argue that our model helps explain the origins of banking in Medieval and Early Modern Europe.     

Tourism Restructuring and the Politics of Sustainability: A Critical View From the European Periphery (The Canary Islands)

There has been considerable discussion of sustainable tourism development, in both theory and practice. Less attention has been paid to the discourses, ideologies and power relations which help to shape 'sustainable' tourism policies and planning instruments. This paper analyses recent initiatives introduced by the regional government of the Canary Islands, designed to bring about a more sustainable model of regional tourism, including a temporary moratorium on tourism development and the drafting of a strategic and normative set of guidelines for sustainable tourism. The paper considers the contested nature of the public debate that accompanied these initiatives within the wider context of the regionally distinct pattern of tourism and capitalist development. It is argued that the legacy of uneven development, and the entrenched power of regional economic and political élites, is likely to undermine the prospects for a just model of sustainable tourism, and to consolidate the continuing privatisation of space and socio-spatial inequalities across the region.     

Optimization Modeling for Logistics: Options and Implementations

Logistics optimization has significantly grown in popularity over the last few decades. Improvements in computing power, modeling software, and the willingness of companies to invest time in the modeling effort have allowed models that were once too unwieldy to solve to optimality to be solved quickly. This has led to a more wide‐spread recognition by logistics managers of the potential advantages of using optimization. The scope of logistics optimization in companies and organizations has expanded to address strategic, tactical, operational, and collaborative decision making. Spreadsheets, an analytical tool familiar to managers, have played a crucial role in the expanded modeling efforts of companies. Although optimization’s role in logistics has grown tremendously, there still are areas that remain to be explored that will allow it to achieve an even larger and more successful role in the management of companies. Additionally, there are some models that are still too large or too complex to currently solve to optimality, despite the advances in computing power and modeling/solving software.     

Knowledge sharing and the dynamics of social capital

Mutual aid often entails the sharing of knowledge. We investigate how, in turn, knowledge sharing affects the long-run dynamics of mutual aid. In our economy, agents with specific knowledge are “held up” by their principals. Inside communities, agents aid each other by sharing their specific knowledge. This process generates a new type of knowledge which exacerbates the specificity of the existing types and induces more agents to engage in mutual aid. However, since the knowledge generated is shared, it progressively renders agents inside communities more flexible and, thus, less dependent on mutual aid. We characterize conditions under which in the long-run mutual aid spreads or is abandoned.     

FORECASTING VOLATILITY WITH THE MULTIFRACTAL RANDOM WALK MODEL

We study the problem of forecasting volatility for the multifractal random walk model. In order to avoid the ill‐posed problem of estimating the correlation length T of the model, we introduce a limiting object defined in a quotient space; formally, this object is an infinite range log volatility. For this object and the nonlimiting object, we obtain precise prediction formulas and we apply them to the problem of forecasting volatility and pricing options with the MRW model in the absence of a reliable estimate of σ and T.     

Rank reduction of correlation matrices by majorization

A novel algorithm is developed for the problem of finding a low-rank correlation matrix nearest to a given correlation matrix. The algorithm is based on majorization and, therefore, it is globally convergent. The algorithm is computationally efficient, is straightforward to implement, and can handle arbitrary weights on the entries of the correlation matrix. A simulation study suggests that majorization compares favourably with competing approaches in terms of the quality of the solution within a fixed computational time. The problem of rank reduction of correlation matrices occurs when pricing a derivative dependent on a large number of assets, where the asset prices are modelled as correlated log-normal processes. Such an application mainly concerns interest rates.     

MODELING UNCERTAIN FORECAST ACCURACY IN SUPPLY CHAINS WITH POSTPONEMENT

We examine a situation where a manufacturer operates in a two‐mode production environment. The first mode could involve overseas vendors and manufacturing facilities. If additional units are later required, the company must use its second mode—more expensive last‐minute domestic vendors and manufacturing sites. We develop a new methodology for analyzing the impact of forecast accuracy on the decision to postpone production. We examine the interaction of forecast accuracy, shortage vs. holding costs, transportation costs and the cost of postponing production in the supply chain of a single product facing uncertain demand. Our model can be used to analyze the cost of important changes, such as increasing forecast accuracy, reducing the cost of backorders, lowering the cost of delaying production, or lowering transportation costs. Our model allows a firm to understand its overall cost structure so that it can accurately evaluate the impact of improved forecast accuracy and lowered costs in the context of postponement.     

Sharing information in the credit market: Contract-level evidence from U.S. firms

We investigate the impact of lenders' information sharing on firms' performance in the credit market using rich contract-level data from a U.S. credit bureau. The staggered entry of lenders into the bureau offers a natural experiment to identify the effect of lenders' improved access to information. Consistent with the predictions of 35 and 36 and Pagano and Jappelli (1993), we find that information sharing reduces contract delinquencies and defaults, especially when firms are informationally opaque. The results also reveal that information sharing does not reduce the use of guarantees, that is, it may not loosen lending standards.